Journal of Water Resources Planning and Management

This study is motivated by multipleobjective optimization in short-term water management for a regional water utility. Although an increasing application of multi-objective evolutionary algorithm (MOEA) has been reported in the literature, we are not aware of its use for short-term water management by water utilities with diverse supply sources. This study presents an innovative practice for determining monthly resource allocation from multiple water supply sources that consider multiple objectives, including deviation from budgeted production, under or overutilization of a given portfolio of resources, and total cost of water production. This method is comprised of a simulation model, namely a production allocation model (PAM) and a MOEA. The decision variables of the MOEA optimization problem are monthly groundwater production from two groundwater wellfields. TheMOEA is used to search for Pareto optimal solutions across different objectives and the PAM uses MOEA output and considers operational constraints to determine water production from the other four supply sources in the decision horizon. Stochastic demand and supply realizations were generated to capture a wide range of uncertainties which were then sampled by a Latin Hyper Cube to make the computation tractable. A parallel computing environment was used to implement this near real-time decision support tool, providing timely guidance for water resources managers. One major difference between this study and many reported in the literature is that the MOEA was used to find Pareto solutions for each demand-supply realization rather than the entire ensemble. This setup allows water resources managers to explicitly explore Pareto solutions based on different supply and demand outlooks. The application of the innovative practice is demonstrated for a regional wholesale water supply utility, Tampa Bay Water, on the west coast of Florida in the United States. One additional advantage of MOEA-assisted planning is that it allows water managers to combine expert judgments and institutional knowledge in identifying solutions. A comparison between MOEA-assisted monthly production planning and heuristic planning reveals that the potential impact of short-term operations, e.g., deviation from budgeted production, is fully considered in a systematic approach. The proposed method can be applied to other regions with similar challenges in water resources management.

Adapting reservoir operation to a changing climate is important to improve water system performance toward benefits including water security, and energy production. However, managers still need to know if and how reoperation can also assist long-term mitigation of climate change impacts on aquatic ecosystems, creating, for example, opportunities to revert fish migration and recruitment losses. This paper investigates the operational adaptive capacity of water systems to mitigate climate change impacts on both hydropower and aquatic ecosystems, with a detailed representation of fish species recruitment response to flow regime changes. The methodology framework combines hydroclimatic modeling, explicit stochastic reservoir operation modeling, and predictive modeling of migratory fish recruitment abundance, illustrated using the large-scale hydropower system of the Paraná River Basin in Brazil. Results identified that operating policies can be adapted to improve hydropower production under a changing climate with drier conditions by 2% to 8% compared with current operating policies. Although insufficient to eliminate all energy losses that climate change may cause, the optimized operation provided flexibility to adjust flow releases and reduce the likelihood of future severe multiyear deficits, which are very harmful to fish populations. Adapting operation to climate change sacrificed fish recruitment performance over a few years of the time horizon to maintain an overall higher storage, but it also improved the chances of producing flow releases in magnitude, timing, and duration during long drier periods that prevented more severe impacts of climate change on fish recruitment and population. This indicates that it might be possible to have synergies in adapting reservoir operation to not only prevent energy losses, but also to improve fish recruitment under climate change. The ecosystem resiliency under adapted operation increased up to 2 times compared with the isolated climate change effect.

Abstract

Hydraulic state estimation (HSE) can be used to infer the flow and pressure regime in water supply systems based on the available measurements in the network and the associated hydraulic model. Because the inputs involved in the process are noisy, uncertainty quantification is paramount to assess the reliability of HSE results. Numerical and analytical methods have been adopted to quantify HSE uncertainty in the past, but they are associated with poor scalability for large networks. The aim of this paper is to adapt the analytical first-order second-moment (FOSM) formulation for HSE uncertainty assessment, which is the most widely adopted method in the literature, by using decomposition techniques to improve its scalability. The decomposed methodology is equivalent to the original formulation and is here applied to several case studies. Computational times were two orders of magnitude lower in large networks thanks to the decomposed formulation, which loses its computational advantage in small/medium-sized systems. Moreover, the numerical conditioning improves when dividing the network. Therefore, the proposed methodology constitutes a better alternative for HSE uncertainty quantification in large networks and could be key to boost HSE implementation in operational systems.

Practical Applications

HSE is known to be a useful monitoring tool for water supply systems. The quality of HSE results is heavily dependent on data and hydraulic model errors, so uncertainty quantification is crucial to assess their reliability. The FOSM method is typically adopted to quantify HSE results’ uncertainty. Even though it is computationally advantageous with respect to Monte Carlo simulations, it still presents poor scalability in large systems due to (1) its high computational expense, and (2) its tendency to numerical ill-conditioning. This work adapts the traditional FOSM formulation so that it can benefit from the available network divisions in large systems. Working in smaller areas (i.e., subnetworks), such as district metered areas, reduces the size of the matrices involved in uncertainty propagation, effectively lowering the execution time and improving numerical conditioning by two orders of magnitude in large systems. Therefore, this new divide and conquer approach improves the applicability of the FOSM philosophy for HSE uncertainty quantification in large networks. Because HSE (and HSE uncertainty quantification) has proved its interest as a decision-making tool, the new method would help water utilities to assess the reliability of their information communication technology (ICT) solutions.

Abstract

Tools used to predict hydraulics and water quality within premise plumbing systems have gained recent interest. An open-source Python-based tool—PPMtools—for modeling and analyzing premise plumbing systems with WNTR or EPANET is presented. A relative water age—the time water has spent in a home—study using three real-world single-family homes was used to demonstrate PPMtools. Results showed that increased use—more people or higher flow fixtures—led to a general decrease in relative water ages. However, even with more use, one user could still experience water for a drinking activity with a relative water age equal to, or longer than, the duration of the longest stagnant period (sleeping or absence from home). Simulations also showed that the general relative water ages increased if the homes were plumbed with larger diameter piping [19.1 mm ($3/4\mathrm{in.}$) versus 12.7 mm ($1/2\mathrm{in.}$)]. Hot water heaters were found to have the largest impact on relative water age. Smaller volume uses generally had more variability in relative water ages, while larger volume uses (e.g., showering) resulted in generally low relative water ages with less variability because larger uses fully replaced water in the home with water from the main. This study highlights the potential for using PPMtools to explore more complex water quality modeling within premise plumbing systems.

Practical Applications

The time water spends (its relative age) in a premise plumbing system (home or building) provides a simple metric for estimating water quality. PPMtools was developed as a framework for simulating premise plumbing systems and was demonstrated using a water age study. PPMtools provides a tool for using EPANET to model these systems and handles fixture, activity, and agent (person or device) level information for model generation and analyzing results. This work demonstrates preliminary findings for three single-family homes with their EPANET input files and discusses their general implications. PPMtools can be used to study other premise plumbing water topics with additional work, where this work demonstrates the foundational work on PPMtools’ development. These results highlight some general trends associated with occupancy level, plumbing sizing, and the impact of low-flow fixtures, and they are discussed in terms of their impact on relative water ages. Results highlight that simplified metrics may fail to capture the full complexity of estimating water age or interuse stagnation periods.

Traditional control and monitoring of water quality in drinking water distribution networks (WDNs) rely on mostly model- or toolbox-driven approaches, where the network topology and parameters are assumed to be known. In contrast, system identification (SysID) algorithms for generic dynamic system models seek to approximate such models using only input-output data without relying on network parameters. The objective of this paper is to investigate SysID algorithms for water quality model approximation. This research problem is challenging due to (1) complex water quality and reaction dynamics; and (2) the mismatch between the requirements of SysID algorithms and the properties of water quality dynamics. In this paper, we present the first attempt to identify water quality models in WDNs using only input-output experimental data and classical SysID methods without knowing any WDN parameters. Properties of water quality models are introduced, the ensuing challenges caused by these properties when identifying water quality models are discussed, and remedial solutions are given. Through case studies, we demonstrate the applicability of SysID algorithms, show the corresponding performance in terms of accuracy and computational time, and explore the possible factors impacting water quality model identification.

Abstract

This paper establishes the minimum amount of energy required by a simple system, a benchmark established on the basis of current technology and the requirements by energy regulators, weighting costs, and efficiency. The first step is to normalize the energy intensity (${\mathrm{kWh}/\mathrm{m}}^3$), which is the quotient between the energy consumed and the volume pumped, with the geometric gradient. The new normalized parameter is more objective than the commonly standardized one with the pumping head due to its dependence on friction. In addition, it makes it possible to naturally disaggregate energy needs (in useful and dissipated energy during transport) to establish a target energy intensity and evaluate existing savings. The quotient between energy intensities (actual and target) allows qualifying transport efficiency. At the same time, it permits extending the policy of energy labeling to pressurized water transport, which has been proven to be a successful energy efficiency strategy.

Practical Applications

The concepts described in this paper establish the minimum amount of energy required by a single system. A simple system is defined as a drive with a single input point and a single output point. The establishment of the minimum amount of energy required allows a reference point to be established, and with this, a possible margin for improvement. These indicators, defined in a simple way and requiring little data, can be of great help to know the current state of simple systems and compare them with the best version of these, as well as with an intermediate state improved on the basis of current technology and the requirements of energy regulators, weighing both costs and efficiency.